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Record W4242398713 · doi:10.1149/ma2019-02/40/1797

Cobalt-Free Perovskite-Type Oxygen Reduction Reaction Catalysts for Energy Conversion

2019· article· en· W4242398713 on OpenAlexaboutno aff
Senthil Velan Venkatesan, O. Abubaker, Kalpana Singh, Venkataraman Thangadurai

Bibliographic record

VenueECS Meeting Abstracts · 2019
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsnot available
Fundersnot available
KeywordsCatalysisPerovskite (structure)OxideTransition metalElectrochemistryInorganic chemistryElectronegativityElectrochemical energy conversionCobaltRotating disk electrodeChemistryAlkaline earth metalMetalMaterials scienceElectrodePhysical chemistryCyclic voltammetryMetallurgyCrystallography

Abstract

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Alkaline fuel cells and metal–air batteries are emerging as an alternative energy conversion devices which can use non-precious and earth-abundant materials as electrocatalysts for oxygen reduction reaction (ORR) [1-3]. Perovskite-type oxides have been widely investigated due to their high activity, long durability and low cost [4]. Mn and Co-based perovskites have shown stable and high ORR performance and their electrochemical properties was explained using the molecular orbital theory. It was shown that the transition-metal-oxide electrocatalysts exhibit maximum catalytic activity when the occupancy of the eg orbital is ~1 which is the primary activity descriptor, whereas spin-state and electronegativity are secondary activity descriptors [5]. However, Co-based oxides have several disadvantages such as high volatility, thermal expansion coefficient, and cost [6-8]. Thus, there is need for alternative Co-free materials for next generation fuel cells. Here, we report Co-free perovskite-type Ba0.5Sr0.5Fe0.8Cu0.2O3-δ as ORR electrode for fuel cells. Effect of Cu-doping in Ba0.5Sr0.5Fe1-xCuxO3-δ (x = 0-0.2) towards the ORR activity in alkaline media has been studied. Partial substitution of Cu in the Fe-site in (Sr,Ba)FeO3 could influence the transition-metal oxygen covalency which replaces and regenerates OH— ions in alkaline ORR process. Thin film rotating-disk electrode (RDE) method with well-defined oxygen transport was previously developed to study the ORR activity of perovskite catalysts [4, 9]. Evaluating the effects of such molecular level engineering on the desired outcome is challenging, since the ORR process consists of multiple adsorption/desorption step involving oxygen containing species. There are fewer literature on understanding the ORR process on transition metal oxides involving the transfer of four electrons and four OH— ions to O2 with subsequent cleavage of double bond O2. Ba0.5Sr0.5Fe1-xCuxO3-δ (x = 0-0.2) powders were prepared through sol gel synthesis method. Powder X-ray diffraction was employed for structural characterization and the activity towards ORR was studied using RDE method. RDE experiments were performed in oxygen saturated electrolyte at 100-1600 rpm. The voltage scan was performed from 0.4-0.9 V vs. RHE. The relation between current and rotational speed was evaluated through a Koutechy-Levich (K-L) plot to deduce the number of electrons (n) involved in the ORR process, i.e. n = (0.62∙F∙D0 2/3∙ v-1/6∙Co∙slope)-1 Where, F is Faraday’s constant, F = 96485 C.mol-1, (Do) diffusion coefficient of oxygen in 0.1M KOH = 1.93x10-5 cm2.s-1, (ν) Kinematic viscosity of 0.1 M KOH = 1.09x10-2 cm2.s-1, (Co) saturation concentration of O2 in 0.1 M KOH at 1 atm O2 pressure = 1.26x10-6 mol.cm-3, and slope – slope from K-L plot. Preliminary results showed that Cu doping in the lattice is affecting the ORR properties of perovskite in alkaline media. The slope of K-L plot gradually increases with increase in Cu content and decreases beyond 0.05 Cu until 0.2 Cu. The number of electron involved in the ORR process is mostly restricted to two. Acknowledgements This research was supported by the Canada First Research Excellence Fund (CFREF) at the University of Calgary. References Lu, J. Pan, A. Huang, L. Zhuang, J. Lu, Proc. Natl. Acad. Sci. U. S. A. 105, 20611 (2008). Pan, C. Chen, L. Zhuang, J. Lu, Acc. Chem. Res. 45, 473 (2012). Oz, K. Singh, D. Gelman, V. Thangadurai, Y. Tsur, J. Phys. Chem. C. 122 (27) 15097 (2018). Ge, A. Sumboja, D. Wuu, T. An, B. Li, F. W.T. Goh, T.S.A. Hor, Y. Zong, Z. Liu, ACS Catal. 5 , 4643 (2015). Suntivich, H. A. Gasteiger, N.Yabuuchi, H. Nakanishi, J.B. Goodenough, Y. Shao-Horn, Nat. Chem. 3, 546 (2011). McIntosh, J.F.Vente, W.G. Haije, D.H.A. Blank, H.J.M. Bouwmeester, Chem. Mater. 18, 2187 (2006). Teraoka, H. Shimokawa, C. Y. Kang, H. Kusaba, K. Sasaki, Solid State Ionics 177 , 2245 (2006). Wang, C. Tablet, A. Feldhoff, J. Caro, Adv. Mater. 17, 1785 (2005). Ge, A. Sumboja, D. Wuu, T. An, B. Li, F. W. T. Goh, et al., ACS Catal. 5, 4643 (2015). Figure 1

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.222
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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